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roberta-finetuned-qa-policy_2 – AI Model by ppsingh | AlphaNeural AI
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ppsingh
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roberta-finetuned-qa-policy_2
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transformers
pytorch
roberta
question-answering
generated_from_trainer
deepset/roberta-base-squad2
finetune
cc-by-4.0
endpoints_compatible
us
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roberta-finetuned-qa-policy_2
This model is a fine-tuned version of
deepset/roberta-base-squad2
on the None dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 32
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 7
Evaluation
This model reaches a F1 score of 58 on the
policy QA
in comparison to 25 when using roberta-base-squad2 base model.
Framework versions
Transformers 4.33.2
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.13.3
